Acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL) and chronic lymphocytic leukemia (CLL) each appear to be heterogeneous at the molecular level (Bloomfield and Caligiuri, 2000). During the past two decades, there has been considerable progress in identifying cytogenetic and molecular markers that can predict outcome following standard treatment for a fraction of patients with AML, ALL, or CLL. However, there still remains a sizable fraction of cases for which molecular predictors of prognosis are not clinically useful, because the malignant clone either lacks such an abnormality or because the abnormality is too infrequent to correlate with clinical outcome. The CALGB is a leader in using cytogenetic and molecular markers to stratify patients based on the risk of failure with standard treatment, and the identification of such indicators has become central to assignment of the appropriate therapy. This trend is likely to become even more critical in the future as more and more targeted therapies that are only active in molecularly defined subsets of patients become available in the clinic. Thus, a very high priority for the CALGB is to develop better and more robust molecular means to predict outcome in the hematologic malignancies. We will do this in conjunction with the Leukemia Committee, as described in that proposal. We hypothesize that genetic expression profiling using microarrays of diagnostic AML, ALL, and CLL samples that lack cytogenetic or molecular markers predictive of clinical outcome can identify a genetic expression profile or "signature pattern" that can be used to predict clinical outcome following standard therapy. A corollary to this hypothesis is that such a molecular profile could then be used for risk stratification of treatment and ultimately to improve clinical outcome in patients with AML, ALL, and CLL. Also, we hypothesize that Restriction Landmark Genomic Scanning (RLGS) can be used to identify prognostically significant DNA methylation patterns in CLL, with the same implications for clinical outcome.